ska_sdp_spectral_line_imaging.data_procs.deconvolution package

ska_sdp_spectral_line_imaging.data_procs.deconvolution.deconvolve(dirty, psf, use_radler=False, **kwargs)[source]

Clean using a variety algorithms.

Parameters:

  • dirty (Image) -- Dirty Image

  • psf (Image) -- Point Spread Function

  • use_radler (bool) -- Use radler to perform deconvolution instead of SDP functions

  • **kwargs -- Keyword arguments

Return type:

Tuple[Image, Image]

Returns:

Tuple[Image, Image]

ska_sdp_spectral_line_imaging.data_procs.deconvolution.restore_cube(model, psf, residual=None, clean_beam={'bmaj': None, 'bmin': None, 'bpa': None})[source]

Note: This documentation copied from ska_sdp_func_python.image.deconvolution.restore_cube.

Restore the model image to the residuals.

The clean beam can be specified as a dictionary with fields "bmaj", "bmin" (both in arcsec) and "bpa" in degrees.

Parameters:

  • model (Image) -- Model image (i.e. deconvolved)

  • psf (Image) -- Input PSF

  • residual (Image, default: None) -- Residual Image

  • clean_beam (dict, default: {'bmaj': None, 'bmin': None, 'bpa': None}) -- Clean beam e.g. {"bmaj":0.1, "bmin":0.05, "bpa":-60.0}. Units are deg, deg, deg

Return type:

Image

Returns:

restored image

Submodules